Final Project

Xinyu (Lily) Wang, Sophie Hartford, Everett Mahaffy

Study/Data Info

We are utilizing data from the longitudinal study “Transitions in Adolescent Girls,” (Barendse et al., 2019) specifically focusing on data from baseline (Wave 1). This dataset includes 174 participants who were 10-13 at the baseline. Our variables of interest are emotion regulation strategy use and depressive symptoms. These were measured using the Emotion Regulation Questionnaire (ERQ) (Gross & John, 2003) and the Center for Epidemiological Studies Depression Scale for Children (CES- DC). (Weissman et al., 1980) These are continuous scales which provide total scores for reappraisal and suppression (ERQ) and depressive symptoms (CES-DC).

Topic Overview

Adolescence is a pivotal period of development characterized by rapid physical, social, and emotional change (Guyer et al., 2016; Nelson et al., 2016) Early adolescence has been proposed as a particularly crucial period for the development of emotion regulation (Silvers, 2022), the processes by which individuals moderate and modulate their emotional experiences and expression (Gross, 1998; Gross, 2015). Additionally, adolescents face heightened vulnerability to the depressive symptoms (Kessler et al., 2007), with depression rates spiking between early and middle adolescence and even steeper rates for girls (Hankin et al., 2007).

Present Study

Drawing upon the longitudinal TAG dataset, we sought to examine whether two particularly salient and well-studied emotion regulation strategies (reappraisal and suppression) were associated with depressive symptoms in early adolescent girls (age 10-13).

These findings will serve as a foundation for future studies investigating these relations across time utilizing all six waves of the data.

Research Questions:

  • Is higher reappraisal use associated with lower depressive symptoms in adolescent girls?
  • Is higher suppression use associated with higher depressive symptoms in adolescent girls?

Load packages

library(tidyverse)
library(flextable)
library(here)
library(readxl)
library(modelsummary)
library(janitor)
library(performance)

Load data

We first had to load in four separate datasets to join which contained age, race, emotion regulation, and depressive symptoms:

# Demographic
age <- read_csv(here("data", "TAG_age_session_dates_4waves.csv"))
age <- clean_names(age)

race <- read_excel(here("data", "TAG_W1_Race_Ethnicity.xlsx"))
race <- clean_names(race)

# Emotion Regulation(ERQ)
erq <- read_csv(here("data", "ERQ_Wave1.csv"))
erq <- erq %>% 
       clean_names() %>% 
       distinct(tagid, .keep_all = TRUE)

# Adolescent Depression(CESDC)
cesdc <- read_csv(here("data", "CESDC_Wave1.csv"))
cesdc <- clean_names(cesdc)

Participant Demographics

Race/ethnicity category

First we had to clean the race/ethnicity data by identifying subjects reported multiple racial categories and rename them to “Multi-racial”

race_clean <- race %>%
  mutate(
    w1_ethnicity = as.character(w1_ethnicity),
    w1_ethnicity = if_else(
      str_detect(w1_ethnicity, ","),
      "g. Multi-racial",
      w1_ethnicity
    )
  )

Race/ethnicity distribution

race_clean %>%
  count(w1_ethnicity) %>%
  ggplot(aes(x = fct_reorder(w1_ethnicity, n), y = n, fill = w1_ethnicity)) +
  geom_col(show.legend = FALSE) +
  coord_flip() +
  labs(
    title = "Distribution of Race/Ethnicity in Sample",
    x = "Race/Ethnicity",
    y = "Number of Participants"
  ) +
  theme_minimal(base_size = 13)

Participant demographics cont.

Age

Each wave consisted of two sessions, so we had to calculate the average age between sessions:

age <- age %>% 
  mutate(W1_mean_age = (w1s1_age + w1s2_age)/2) 

# Check mean age for sample
age_summary <- age %>%
  summarize(
    mean_W1_age = mean(W1_mean_age, na.rm = TRUE),
    sd_W1_age   = sd(W1_mean_age, na.rm = TRUE),
  )
print(age_summary)
# A tibble: 1 × 2
  mean_W1_age sd_W1_age
        <dbl>     <dbl>
1        11.6     0.808

Joining all datasets

df <- erq %>% 
  left_join(cesdc, by= 'tagid')

df <- df %>% 
     left_join(age, by = 'tagid')

df <- df %>% 
     left_join(race_clean, by = 'tagid') %>% 
     select(tagid, erq_reappraisal_total, erq_suppression_total, ces_dc_total_75perc, W1_mean_age, w1_ethnicity, 
            w1s1_date, w1s1_age, w1s2_date, w1s2_age, w2s1_date, w2s1_age, w2s2_date, w2s2_age, 
            w3s1_date, w3s1_age, w3s2_date, w3s2_age, w4s1_date, w4s1_age, w4s2_date, w4s2_age
            )

Renaming

In order to tidy the data, we first had to rename each wave/session/date/age variable

df <- df %>%
  rename(
    wave1_session1_date = w1s1_date,
    wave1_session1_age  = w1s1_age,
    wave1_session2_date = w1s2_date,
    wave1_session2_age  = w1s2_age,

    wave2_session1_date = w2s1_date,
    wave2_session1_age  = w2s1_age,
    wave2_session2_date = w2s2_date,
    wave2_session2_age  = w2s2_age,

    wave3_session1_date = w3s1_date,
    wave3_session1_age  = w3s1_age,
    wave3_session2_date = w3s2_date,
    wave3_session2_age  = w3s2_age,

    wave4_session1_date = w4s1_date,
    wave4_session1_age  = w4s1_age,
    wave4_session2_date = w4s2_date,
    wave4_session2_age  = w4s2_age
  )

Tidy and filter

# Tidy
df2 <- df %>%
  mutate(across(starts_with("wave"), as.character))

df_long <- df2 %>%
  pivot_longer(
    cols = starts_with("wave"),
    names_to = c("wave", "session", "info"),
    names_sep = "_",
    values_to = "value"
  ) %>%
  mutate(
    wave    = parse_number(wave),
    session = parse_number(session)
  )

df_tidy <- df_long %>%
  pivot_wider(
    names_from = info,
    values_from = value
  ) %>% 
  mutate(age = as.numeric(age))


# Filter
df_final <- df_tidy %>% 
  filter(wave == 1, session == 1)

Results

Cohort Plot

Sample Summary Statistics

df_names <- df_tidy %>% 
    select(-wave, -session) %>% 
    rename(
    Reappraisal       = erq_reappraisal_total,
    Suppression       = erq_suppression_total,
    Depression        = ces_dc_total_75perc,
    Age               = W1_mean_age,
    Ethnicity         = w1_ethnicity
    )

Summary Stats Table

datasummary_skim(df_names,
                 title = "Table 1. Sample Descriptive Statistics")
Unique Missing Pct. Mean SD Min Median Max Histogram
Reappraisal 31 2 27.4 6.6 6.0 28.0 41.0
Suppression 24 2 13.7 5.2 5.0 13.0 28.0
Depression 49 5 12.8 10.3 0.0 9.5 50.0
Age 141 1 11.6 0.8 10.0 11.7 13.1
age 893 15 13.8 2.0 10.0 13.7 19.0
Ethnicity N %
a. Black/ African American 8 0.7
b. Hispanic/ Latino/ Chicano 48 4.2
c. Native American or Native Alaskan 8 0.7
d. White / Caucasian 720 63.4
e. Asian 16 1.4
g. Multi-racial 248 21.8
h. Other 16 1.4
j. Decline to respond 8 0.7
NA 64 5.6

Primary RQs

Is higher emotion reappraisal associated with lower depression symptoms in adolescent girls?

rq1 <- lm(df_tidy$ces_dc_total_75perc ~ df_tidy$erq_reappraisal_total)

Is higher emotion suppression associated with higher depression symptoms in adolescent girls?

rq2 <- lm(df_tidy$ces_dc_total_75perc ~ df_tidy$erq_suppression_total)

Exploratory RQs:

Is higher suppression use associated with higher depression symptoms when adjusting for reappraisal use?

ex1 <- lm(df_tidy$ces_dc_total_75perc ~ df_tidy$erq_suppression_total + df_tidy$erq_reappraisal_total)

Does age moderate the relation between reappraisal/suppression and depression symptoms in adolescent girls?

ex2 <- lm(df_tidy$ces_dc_total_75perc ~ df_tidy$erq_reappraisal_total * df_tidy$W1_mean_age)

ex3 <- lm(df_tidy$ces_dc_total_75perc ~ df_tidy$erq_suppression_total * df_tidy$W1_mean_age)

Primary RQ Model Summaries

Table 2. Model Summary for RQ1 and RQ2
Reappraisal Suppression
+ p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001
Intercept 22.805*** 3.864***
(1.306) (0.800)
Reappraisal (ERQ) -0.362***
(0.046)
Suppression (ERQ) 0.645***
(0.055)

Exploratory Results

Table 3. Model Summaries for Multiple Regression and Interaction Models
Suppression + Reappraisal Reappraisal × Age Interaction Suppression × Age Interaction
+ p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001
Intercept 13.733*** 40.986* 2.701
(1.484) (18.413) (11.043)
Reappraisal (ERQ) -0.332*** -1.688**
(0.043) (0.644)
Suppression (ERQ) 0.592*** 0.053
(0.055) (0.743)
Reappraisal x Age 0.116*
(0.056)
Suppression x Age 0.049
(0.064)

Plot 1

This plot shows the relation between reappraisal and depressive symptoms

Plot 2

This plot shows the relation between suppression and depressive symptoms

Reflections

  • Most of our time was spent cleaning/managing the data - the models were quick!
  • Pivoting can be challenging and renaming can help
  • Don’t forget to make sure your variables are numeric after pivoting

Future Directions

  • Investigate longitudinal association between emotion regulation and depressive symptoms among adolescent girls.
  • Examine potential moderators and mediators such as brain circuit, puberty stage, and socioeconomic factors.
  • Test the reproducibility of the results using other dataset with larger sample size.

References

Barendse, M. E. A., Vijayakumar, N., Byrne, M. L., Flannery, J. E., Cheng, T. W., Flournoy, J. C., Nelson, B. W., Cosme, D., Mobasser, A., Chavez, S. J., Hval, L., Brady, B., Nadel, H., Helzer, A., Shirtcliff, E. A., Allen, N. B., & Pfeifer, J. H. (2019). Study Protocol: Transitions in Adolescent Girls (TAG). Frontiers in Psychiatry, 10, 1018. https://doi.org/10.3389/fpsyt.2019.01018
Gross, J. J. (1998). The emerging field of emotion regulation: An integrative review. https://doi.org/10.1037/1089-2680.2.3.271
Gross, J. J. (2015). Emotion regulation: current status and future prospects. Psychological Inquiry, 26(1), 1–26. https://doi.org/10.1080/1047840X.2014.940781
Gross, J. J., & John, O. P. (2003). Individual differences in two emotion regulation processes: Implications for affect, relationships, and well-being. Journal of Personality and Social Psychology, 85(2), 348–362. https://doi.org/10.1037/0022-3514.85.2.348
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Hankin, B. L., Mermelstein, R., & Roesch, L. (2007). Sex differences in adolescent depression: stress exposure and reactivity models. Child Development, 78(1), 279–295. https://doi.org/10.1111/j.1467-8624.2007.00997.x
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Nelson, E. E., Jarcho, J. M., & Guyer, A. E. (2016). Social re-orientation and brain development: An expanded and updated view. Developmental Cognitive Neuroscience, 17, 118–127. https://doi.org/10.1016/j.dcn.2015.12.008
Silvers, J. A. (2022). Adolescence as a pivotal period for emotion regulation development. Current Opinion in Psychology, 44, 258–263. https://doi.org/10.1016/j.copsyc.2021.09.023
Weissman, M. M., Orvaschel, H., & Padian, N. (1980). Children’s symptom and social functioning self-report scales. Comparison of mothers’ and children’s reports. The Journal of Nervous and Mental Disease, 168(12), 736–740. https://doi.org/10.1097/00005053-198012000-00005